Classical machine learning has become ubiquitous for practical pattern recognition and predictive analytics tasks, powering applications from image classification to stock market forecasting. Machine learning models like neural networks can make predictions and decisions without relying on predefined rigid program logic by learning from sample data (Wang and Liu in A comprehensive review of quantum machine learning: From NISQ to fault tolerance, 2024 [1]).